Evaluation of grazing impact on land degradation processes is a difficult task due to heterogeneity and complex interacting factors involved. In this research, we designed a new methodology based on a predictive index of GSLDI (Grazing Susceptibility to Land Degradation Index) built on artificial intelligence to assess land degradation susceptibility in areas affected by small ruminants (SRs) of sheep and goats grazing impacts. The data for model training, validation, and testing consisted of sampling points (erosion and no-erosion) taken from aerial imagery. Seventeen environmental factors (e.g., DEM derivatives, small ruminants’ stock), and 55 subsequent attributes (e.g., classes/features) were assigned to each sampling point. The impact of SRs stock density over the land degradation process has been evaluated and estimated with two extreme SRs’ density scenarios: missing (0), and double density (overstocking). We applied the GSLDI methodology on the Curvature Subcarpathians, a region that experiences the highest erosion rates in Romania, and found that SRs grazing is not the major contributor to land degradation, accounting for only 4.6%. This methodology could be replicated in other steep slopes grazing areas as a tool to assess and predict areas susceptible to land degradation, and to establish common strategies for good land-use practices.